We have just passed the annual maximum in Arctic sea ice extent which always occurs sometime in March. Within a month we will reach the annual maximum in Arctic sea ice volume. After that, the sea ice will begin its course towards its annual minimum of both extent and volume in mid-September. This marks the beginning of the ritual of the annual sea ice watch that includes predictions of the extent and rank of this year’s sea ice minimum, as well as discussion about the timing of its eventual demise. One of the inputs into that discussion is the “PIOMAS” ice-ocean model output of ice volume – and in particular, some high-profile extrapolations. This is worth looking at in some detail.

Prediction methods for the sea ice minima range from ad-hoc guesses to model predictions, from statistical analyses to water-cooler speculation in the blogosphere. Many of these predictions are compiled in the SEARCH-sponsored “sea ice outlook“.

This year’s discussions however will be without the input of the father of modern sea ice physics, Norbert Untersteiner, who recently died at the age of 86. Much of the physics in PIOMAS and global climate models can be traced to Norbert’s influence. Norbert was sober-minded and skeptical about the prospects of skillful short-term sea ice predictions, but even he couldn’t help but be drawn into the dubious excitement around the precipitous decline of arctic sea ice and regularly added his own guestimate to the sea ice outlook. Norbert’s legacy challenges those of us who engage in predictions to prove our skill and to understand and explain the limitations of our techniques so they are not used erroneously to misinform the public or to influence policy…more about that later and here.

PIOMAS

PIOMAS is the Panarctic Ice Ocean Modeling and Assimilation System. It belongs to the class of ice-ocean models that have components for the sea ice and the ocean, but no interactive atmosphere. There is an active community (AOMIP) engaged in applying and improving these types of models for Arctic problems. Without an atmosphere, inputs that represent the atmospheric forcing (near surface winds, temperature, humidity, and downwelling short and longwave radiation) need to be provided. Typically those inputs are derived from global atmospheric reanalysis projects. The advantage of such partially-coupled models is that they can be driven by past atmospheric conditions and the simulations match well the observed sea ice variability, which is strongly forced by the atmosphere.

This is in contrast to fully-coupled models, such as those used in the IPCC projections, which make their own version of the weather and can only be expected to approximate the mean and general patterns of variability and the long-term trajectory of the sea ice evolution. Another advantage of ice-ocean models is that they don’t have to deal with the complexities of a fully-coupled system. For example, fully-coupled models have biases in the mean wind field over the Arctic which may drive the sea ice into the wrong places, yielding unrealistic patterns of sea ice thickness. This has been a common problem with global climate models but the recent generation of models clearly shows improvement. Because sea ice is strongly driven by the atmosphere, model predictions depend on the quality of the future atmospheric conditions. Therefore an ice-ocean model, like PIOMAS, is much more accurate at hindcasts, when the atmospheric conditions are simply reconstructed from observations, than for forecasts, when atmospheric conditions must be estimated. That is not to say that PIOMAS can’t be used for predictions, it can (Zhang et al. 2008, Lindsay et al. 2008 , Zhang et al. 2010) but it is important to recognize that performance at hindcasts does not necessarily say much about performance at forecasts. This point often gets confused.

Figure 1: PIOMAS mean monthly arctic sea ice volume for April and September. Dashed lines parallel to linear fits represent one and two standard deviations from the trend. Error bars are estimated based on comparison with thickness observations and model sensitivity studies (Schweiger et al. 2011)

PIOMAS was developed and is operated by Jinlun Zhang at the University of Washington. It is the regional version of the global ice-ocean model of Zhang and Rothrock (2003). The sea ice component represents sea ice in multiple categories of thickness and accounts for changes in thickness due to growth and melt as well as mechanical deformation of ice (Thorndike et al. 1975, Hibler 1980).
It has evolved with continual improvements, including the addition of data assimilation capabilities (Zhang et al. 2003, Lindsay et al. 2006) and the development of sister models for new applications (BIOMAS for biology) or specific regions (BESTMAS for the Bering Sea and GIOMAS for the entire globe) (publications). As a modeler working among observationalists from a variety of disciplines, Jinlun has never been short of tire-kickers who probe, push, and challenge his model from all sorts of different angles and identify warts and beauty spots. This is one of the reasons why PIOMAS has evolved into one of the premier ice-ocean models (Johnson et al. 2012), particularly when it comes to the representation of the sea-ice cover.

PIOMAS has been used in a wide range of applications but arguably the most popular product has been the time series of total Arctic sea ice volume which we have been putting out since March 2010 (see also Fig 1). The motivation for this time series is to visualize the fact that the long term Arctic-wide loss of sea ice is not only happening in extent, which is well measured by satellites, but also in thickness, which isn’t. Ice volume, the product of sea ice area and thickness, is a measure for the total loss in sea ice and the total amount of energy involved in melting the ice. Though this is a very small part of the change of global energy content, it is regionally important and investigations into the cause of sea ice need to pin down the sources of this energy.

But why use PIOMAS to show the decline in ice volume when our group of researchers has been involved in measuring, rescuing, and collecting sea ice thickness data from in-situ observations for 30-some years? The answer is that even though wide-spread thickness losses from observations alone have been apparent for some areas or time periods, Arctic-wide thickness losses are more difficult to document because of the sparse sampling in time and space. The problem can be visualized by constructing a “naïve” sea ice thickness time series from in-situ observations:

Figure 2 Naïve sea ice thickness time series. Sea ice thickness observations from the sea ice thickness climate data record (small grey dots), averages for all observations in a given year (large grey dots), and 5-year running mean through those observations. The same calculation for the corresponding PIOMAS simulations at the location and time of observation is shown by the big red dots and line.

Before those claiming that global warming stopped in 1998 have a field day with this figure, they should appreciate that our total volume time series and the naïve thickness time series are entirely consistent. The sampling issues arise from the fact that sea ice is highly dynamic with lots of spatial and seasonal variability so that measurements from individual moorings, submarine sonar tracks, and aircraft flights can only construct an incomplete picture of the evolution of the total Arctic sea ice volume. Progress towards establishing ice thickness records from satellite (ICESat, Envisat, and CryoSat-2) will change this over time, but these sources won’t yield a record before these measurements began and satellite retrievals of ice thickness have their own issues.

PIOMAS is not normally run as a freely-evolving model, but rather it assimilates observations. Ice concentration and sea surface temperature are currently assimilated and we have experimented with the assimilation of ice motion (Zhang et al. 2003, Lindsay et al. 2006). Assimilation helps constrain the ice extent to observations and helps improve the simulation of sea ice thickness. Ice thickness observations are not assimilated into the model. Instead, ice thickness and buoy drift data are used for model calibration and evaluation. So using a model constrained by observations is quite possibly the best we can do to establish a long-term ice volume record.

Model calibration is of course necessary. We need to determine parameters that are not well known, deal with inadequately modeled physics, and address significant biases in the forcing fields. Parameters changed in PIOMAS calibration are typically the surface albedo and roughness, and the ice strength. Once calibrated, the model can be run and evaluated against observations not included in the calibration process. Evaluation does not only mean showing that PIOMAS says something useful but also establishes the error bars on the estimated ice thickness. To establish this uncertainty in the ice-volume record (Schweiger et al. 2011), we spent a significant effort drawing on most types of available observations of ice thickness thanks to a convenient compilation of ice thickness data (Lindsay, 2010). We have also compared PIOMAS estimates with measurements from ICESat and conducted a number of model sensitivity studies. As a result of this evaluation our conservative estimates of the uncertainty of the linear ice volume trend from 1979-present is about 30%. While there is lots to do in improving both measurements and models to reduce the uncertainty in modeled ice volume, we can also say with great confidence that the decline in observed ice thickness is not just an effect of measurement sampling and that the total sea ice volume has been declining over the past 32 years at astonishing rates (for instance a 75% reduction in September volume from 1979 to 2011).

Prediction

The seasonal prediction issue and the prediction of the long-term trajectory are fundamentally different problems. Seasonal prediction, say predicting September ice extent in March, is what is called an initial value problem and the September ice extent depends both on the weather, which is mostly unpredictable beyond 10 days or so, and the state of the ocean and sea ice in March. Improving observations to better characterize that state, and improving models to carry this information forward in time is our best hope to improve seasonal predictability. The prediction of the long-term trajectory, depends on the climate forcing (greenhouse gases, aerosols, solar variability) and how the model responds to those forcings via feedbacks. A recent model study showed that the crossover between initial-value and climate-forced predictability for sea ice occurs at about 3 years (Blanchard-Wrigglesworth et al. 2011). In other words, a model forgets the initial sea ice state after a few years at which point the main driver of any predictable signal is the climate forcing. In fact, coupled model simulations have shown that even removing all the sea ice in a particular July has little lasting impact on the trajectory of the ice after a few years (Tietsche et al. 2011).

PIOMAS has been run in a forward mode (and hence without data assimilation) to yield seasonal predictions for the sea ice outlook (Zhang et al. 2008) and has also provided input to statistical forecasts (Lindsay et al. 2008) and fully-coupled models. We have also done experiments with PIOMAS in a climate projection mode by scaling atmospheric forcing data from a reanalysis to 2xC02 projections from the CMIP3 models (Zhang et al. 2010). This setup provides more realistic wind fields and spatial thickness distribution but cannot account for important atmosphere-ocean feedbacks.

Global climate model projections (in CMIP3 at least) appear to underestimate sea ice extent losses with respect to observations, though this is not universally true for all models and some of them actually have ensemble spreads that are compatible with PIOMAS ice volume estimates and satellite observations of sea ice extent. With error bars provided, we can use the PIOMAS ice volume time series as a proxy record for reality and compare it against sea-ice simulations in global climate models. This provides another tool in addition to more directly observed properties for the improvement and evaluation of these models and is in our view the best use of PIOMAS in the context of predicting the long-term trajectory of sea ice.

Predictions of a seasonally ice-free Arctic Ocean

The eventual demise of the summer sea ice is a common feature of nearly every climate model projection (the exceptions are models with very inappropriate initial conditions). But the question of when the Arctic will be ‘ice-free’ is imprecise and calls for a clear definition of what ice-free means. Does it mean completely ice-free, or is there a minimum threshold implied? Does it mean the first time the summer sea ice goes beneath this threshold or does it imply a probability of encountering low-ice conditions over a period of time? (e.g. high likelihood of Septembers with less than 106 km2 of ice in a 10-year period). Regardless of whether the concept is actually useful for any practical purpose (say for planning shipping across the Arctic), it is nevertheless a powerful image in communicating the dramatic changes that are under way in the Arctic.

Once defined, predictions of when an ice-free Arctic will occur seem justified. In the published literature there are several papers specifically targeting such predictions (Zhang and Walsh, 2006, Wang and Overland, 2009, Boe et al. 2009, Zhang et. al. 2010) while others include discussion about the timing of ice-free summers (e.g. Holland et al. 2006). Some address the fact that the CMIP3/IPCC AR4 simulations show sea ice declines less rapid than the observations and attempt to correct for it. Published projections, though with varying definitions of what constitutes ice-free, all project an ice-free Arctic ocean somewhere between 2037 (Wang and Overland, 2009) and the end of the century. Predictions of earlier ice-free dates so-far seem to be confined to conference presentations, media-coverage, the blogosphere, and testimony before to the UK parliament.

Extrapolation

A different class of predictions are based on simple extrapolation using historical sea ice extent, concentration, or volume. An example is included in the materials presented by the so-called ‘Arctic Methane Emergency Group’ who show extrapolations of PIOMAS data and warn about the potential of a seasonally ice-free Arctic ocean in just a few years. So does it make sense to extrapolate sea ice volume for prediction? In order to do a successful extrapolation several conditions need to be met. First, an appropriate function for the extrapolation should be chosen. This function needs to either be based on the underlying physics of the system or needs to be justified as appropriate for future projections beyond just fitting the historical data.

But what function should one choose? Since we don’t really have data on how the trajectory of the Arctic sea ice evolves under increased greenhouse forcing, model projections may provide a guide about the shape of appropriate function. Clearly, linear, quadratic or exponential functions do not properly reflect the flattening of the trajectory in the next few decades seen for example in the CCSM4 (Fig 3). The characteristic flattening of this trajectory, at first order, arises from the fact that there is an increasingly negative (damping) feedback as the sea ice thins described by Bitz and Roe (2004) and Armour et al. (2011). The thick ice along the northern coast of Greenland is unusually persistent because there are on-shore winds that cause the ice to drift and pile-up there. So extrapolations by fitting a function that resembles a sigmoid-shaped trajectory may make more sense, but even that, as shown in the figure, yields a much earlier prediction of an ice-free Arctic than can be expected from the CCSM4 ensemble.

Figure 3. CCSM4 AR4 ensemble and PIOMAS September mean arctic ice volume. Exponential and sigmoid (Gompertz) fits to PIOMAS data are shown. Note that the 1979-2011 September mean of the CCSM4 ensemble has about 30% higher sea ice volume than PIOMAS. To visualize the difficulty in choosing an appropriate extrapolation function based on PIOMAS data we shifted the CCSM4 time series forward by 20 years to roughly match the mean ice volume over the 1979-2011 fitting period.

But there is a second issue that may foil prediction by extrapolation: The period over which the function is fit must be sufficiently long to include adequate long-term natural variability in the climate system. The goodness of fit over the fitting period unfortunately may be misleading. Whether or not this is the case for sea ice extent or volume is an open question. The sea ice trajectory shows considerable natural variability at various time scales on top of the smoother forced response to changes in greenhouse gases. Periods of rapid decline are followed by slower periods of decline or increases. By fitting a smooth function to a sea ice time series (e.g. PIOMAS) one might easily be tempted to assume that the smooth fit represents the forced (e.g. greenhouse) component and the variation about the curve is due to natural variability. But natural variability can occur at time scales long enough to affect the fit. We have to remember that part of the observed trend is likely due to natural variability (Kay et al. 2011, Winton, 2011) and may therefore have little to do with the future evolution of the sea ice trajectory. This is visualized in figure 4 where ensemble members from the CCSM4 AR4 runs are fit with S-shaped (Gompertz) functions using the 1979-2011 period to estimate the parameters. The differences between the ensemble members, reflecting natural variability, yield vastly different extrapolated trajectories. Natural variability at these time scales (order of 30 years) may very well make prediction by extrapolation hopeless.

In summary, we think that expressing concern about the future of the Arctic by highlighting only the earliest estimates of an ice-free Arctic is misdirected. Instead, serious effort should be devoted to making detailed seasonal-to-interannual (initial-value) predictions with careful evaluations of their skill and better estimates of the climate-forced projections and their uncertainties, both of which are of considerable value to society. Some effort should also target the formulation of applicable and answerable questions that can help focus modeling efforts. We believe that substantially skillful prediction can only be achieved with models, and therefore effort should be given to improving predictive modeling activities. The best role of observations in prediction is to improve, test, and initialize models.

But when will the Arctic be ice free then? The answer will have to come from fully coupled climate models. Only they can account for the non-linear behavior of the trajectory of the sea ice evolution and put longer term changes in the context of expected natural variability. The sea ice simulations in the CMIP5 models are currently being analyzed. This analysis will reveal new insights about model biases, their causes, and about the role of natural variability in long-term change.It is possible that this analysis will change the predicted timing of the “ice free summers” but large uncertainties will likely remain. Until then, we believe, we need to let science run
its course and let previous model-based predictions of somewhere between “2040 and 2100″ stand”

Ray,
Quite conceivable. The ice will also be retreating into colder regions, and encountering less ocean current. The possibility of volcanic activity would also alter the changes. All these competing effects will render most modeling virtually useless when it comes to actual numbers. As Neils Bohr once said, “Prediction is very difficult, especially about the future.”

“We believe that substantially skillful prediction can only be achieved with models, and therefore effort should be given to improving predictive modeling activities. The best role of observations in prediction is to improve, test, and initialize models.”

Coming from leaders in the field of climate science, your words frighten me down to the marrow.

I am a statistician who used to devise models for a bond rating agency. Models are useful tools but the financial crisis taught me (again) that, when the downside is steep and reality is very complex, we cannot afford to base our decisions on substantially skillful prediction. We must be very attentive to observations that warn us that extreme loss scenarios are more probable than our models predict.

@350limit
How do you make predictions without models? Genuinely interested. All the cherry picked quote you have selected says is that prediction of changes for the phenomenon in question we need the best models. Hardly earth shattering stuff.

As for climate science in the round, models are just one line of enquiry so policy decisions are not based wholly on them. I suggest you read up some more.

As Neven points out, in #9, the effects of a collapse in sea ice extent will be felt long before the sea ice totally disappears.

Arctic warming is now driven largely by the albedo flip effect, as ice is replaced by water thus increasing the absorption of sunlight; see paper by Hudson [1]. In the past three decades, the sea ice September extent has declined 40%. If there is a collapse in extent by a further 40%, that would double the albedo flip effect for that month. But the other months could have similar doubling, e.g. as a 20% decline increases to 40%. This would mean that overall the albedo forcing would double, and the rate of Arctic warming would suddenly double.

[Response: This actually doesn’t make much sense. The energy for ice melting is coming from mainly long wave effects and some ocean heat transports, the albedo effect adds to that of course (but less than you might think because of cloud compensation), but a doubling of the albedo effect will not double the rate of melting. And since this is all standard stuff, it is all included in the GCMs, and yet they don’t show the kind of behaviour you are postulating. – gavin]

Now, how soon could such a collapse occur? Here we can look at the PIOMAS volume, and consider how much further thinning can occur before the sea ice becomes so thin over most of the ocean that it will break up and melt away. The extent cannot hold out for many years if the thinning continues. The volume trend suggests that the thinning rate is increasing, indicating that a collapse in extent is likely to occur by 2015. The reliability of this assertion, and the evidence leading to this assertion, has been confirmed by leading sea ice expert, Professor Peter Wadhams, in his evidence to the UK Environment Audit Committee in their hearing on “Protecting the Arctic” [2].

If wind and weather conditions this year produce a decline in volume as between Sept 2009 and Sept 2010, then there could be a collapse in extent this year.

A collapse in sea ice extent would be a catastrophe for Arctic ecosystems and for creatures depending on the sea ice. But more worrying still, the sea ice is critical for holding back methane emissions from the shallow seas of the East Siberian Arctic Shelf (ESAS). When the sea ice is not present, storms can churn up the water, warming the seabed and thawing the frozen structures holding back the methane. Vast plumes, a kilometre across, have been seen in part of the ESAS area. There is a real danger of a destabilisation of methane-holding structures producing an escalation of methane emissions if the sea ice area were to further retreat. Unfortunately there is so much methane held under the ESAS seabed that only a small proportion (perhaps as little as 1%) released into the atmosphere could cause “methane feedback”: a vicious cycle of greenhouse warming [2] and further release.

[Response: This doesn’t make much sense either. CH4 is a small fraction of the warming drivers, and as we saw with David’s posts earlier, you need to have absolutely enormous methane releases to even make a dent. There is no evidence for that now, or in the recent past when Arctic sea ice was less extensive (specifically the Early Holocene or the Eemian). – gavin]

Thus the reluctant conclusion of Wadhams, and other members of the Arctic Methane Emergency Group, AMEG [3], is that drastic emergency measures including geoengineering must be taken as soon as possible in order to cool the Arctic rapidly and minimise the risk of sea ice collapse and methane feedback.

[Response: It is not a conclusion drawn by many others, and if you want to make a case for it, you need to do a much more quantified job. – gavin]

Note that, even if the situation turns out not be quite so dire, action should be taken on the precautionary principle; see Douglas’s comment #33. Why do advising bodies, such as IPCC, always seem to rely on optimistic forecasts, based on out-of-date models, in order to inform politicians of the situation?

[Response: Would you have them report on anything other than the current state of the science? Everything in IPCC has to be traceable to peer-reviewed science, and so that is what they will reflect. Note too that IPCC does not advocate for policy which is what you appear to want it to do. – gavin]

Thanks for your responses. I guess I would say that the longest term extrapolation is about 30 years for the September sea ice behavior, about as long as the data set. It is risky to extrapolate that far. But, in some sense, we are treating the real world as a model realization when we do that, assuming that a system driven into motion may continue in motion if it continues to be driven. The numrical models also assume continued driving. And, the driving does seem to be more important than the non-linearities at least over those timescales. You may be able to get J. Richard Gott III at Princeton to write the paper you’d like to read. But, I don’t have a lot of confidence in the long extrapolation.

What I notice is that consistent corrections to the model, and attention to the behavior of the individual ensemble members brings model projections and the long extrapolation into agreement (#44) while short extrapolations probably should not be attacked based on possible low frequency variability owing to a scale mismatch. If the system has a tendency to hold trends, then a short extrapolation will have some predictive power. And, it seems to be the short extrapolations you are critisizing and perhaps on the wrong basis.

One other difference between the models and PIOMAS that I notice is that the largest ice volume anomaly excursions have been prior to minimum ice volume around July while Varvus et al. note a shift occuring to later minumum ice in the models. Perhaps there is a clue there? Is lower albedo first year ice playing a larger role near solstice than modelled perhaps?

Reports of recent “vast plumes of methane” are found in blogs and newspapers, but nothing Scholar turns up supports these stories. I’d like to see a first person account that can be fact-checked. Scholar finds e.g.

“From the characteristic vertical decrease of methane towards the sea surface, it is concluded that biota are extensively using this energy pool and reducing the methane concentration within the water column by about 98% between 300 m depth and the sea surface. Degassing to the atmosphere is minimal based on the shape of the methane concentration gradient.”http://www.springerlink.com/content/t06u8r17j2897340/

The above was published about 18 years ago; cited by about 20 papers.

This modeling study is consistent with that. I’d be inclined to look for trends in the microorganisms that consume methane.

“… Given the present bulk removal pattern, methane does not penetrate far from emission sites. …. a potential for material restrictions to broaden the perturbations, since methanotrophic consumers require nutrients and trace metals. When such factors are considered, methane buildup within the Arctic basin is enhanced. However, freshened polar surface waters act as a barrier to atmospheric transfer, diverting products into the deep return flow.”

Maybe we can hope for a bright green Arctic Ocean — a big increase in primary production, a big new CO2 sink, and a large new food source — if we can avoid polluting the area with industrial activity.

Dan H., #48–“If volumetric losses remain constant, then surface area and thickness losses must accelerate. Does this make sense?” Well, that’s what I said, too, so yes, it makes sense.

But let me note that while it may be the case that an ice-cube melting in a glass may tend to decrease similarly in all dimensions, it’s definitely not the case for the Arctic ice pack–thinning is clearly proceeding more rapidly than the decrease in area.

Perhaps I’ve misread Tietsche, but I don’t think so. It’s actually pretty hard to misinterpret ‘recover’ in this context. They specifically state that from 2000 on extent and volume “recover” to typical values by the 4th year. I see no evidence in their Figure 1 that any significant percentage of the ensemble exhibited anything like the real-world results we’ve witnessed post-2007. No note, for example, saying: In some ensemble members volume never recovered, but instead decreased at an accelerated rate. Perhaps that caveat was evident in the data – if so, it never made it into the paper. Instead we read:

3. Results
3.1. Sea-Ice Extent and Temperature Anomalies
All our experiments start from sea-ice free conditions on 1st July. As expected, the Arctic Ocean remains ice-free for several months, and significant sea-ice cover does not develop before November. However, sea ice then grows very rapidly, since the growth rate for thin ice is much higher than for thick ice, which acts as a negative feedback on thickness during the growth season (Bitz and Roe, 2004; Notz, 2009). The ensemble mean September ice extent reaches values typical for the reference run in the fifth year after the perturbation for the 1980 time slice, in the fourth year for 2000, and already in the second year for 2020 and 2040 (Figure 1). September sea-ice volume takes longer to recover in the late 20th century when the sea ice is still thick, but it has the same time scale of recovery as sea-ice extent from 2000 on…

Axel. Thanks for the reference (and in an accessible form)! So if I have parsed the proper takaway conclusions. BC does have an effect, but BC inventories have been decreasing, so to the extent that BC contributes to melting, the effect is counter to the recent ice loss accelleration.
I find the BC declines a bit unexpected (for me at least), as I thought increased Chinese emissions were overwhelming other reductions.

The acceleration seems to date from around 2000 rather than 2007. The four years following 2007 had two minima above and two below the 2007 minimum, an even split though the downward trend seems to persist. It is not really as though everything started to happen after 2007. Tietsche et al. had similar behavior in their _reference_ run. The zero ice in July experiment they conducted is quite a different behavior that what we saw in 2007.

I don’t know this for a fact, but I suspect Chinese emissions of black carbon may *not* be increasing. It’s been well-publicized that they have been adding lots of coal-fired generation, but less-well known that the policy is to add modern, much more efficient plants and retire the dirtier, older ones. They are aware of, and concerned about, the high human and monetary costs of their filthy air. (The Economist, for example, recently reported the annual death toll due to air pollution to be something like 200,000.)

“Norbert’s legacy challenges those of us who engage in predictions to prove our skill and to understand and explain the limitations of our techniques so they are not used erroneously to misinform the public or to influence policy”

I’m sure that future generations will thank you for your caution about influencing policy in a way that might mitigate the disasters they will face.

Seriously, I think you should stick with doing the best science you can and leave the issue of influencing policy to others, because I don’t think you understand its basics. The influencing of policy in re global warming faces a huge amount of inertia, but principles of risk mitigation tell us that we should be aggressive about shifting policy to avert possible threats, the opposite of what results from that inertia. And in the face of scientific uncertainty we should be even more aggressive … the more uncertain, the more aggressive we should be.

“Reality is only one realization of an ensemble” – I realise this is in jest, but it does betray an unfortunate mindset.

Regardless of whether you use a linear or sigmoid extrapolation or a CCSM4 AR4 ensemble, you are using a model. George Box’s famous quote is, “All models are wrong; some models are useful”. It seems that CCSM4 models, whether time shifted or not, fail to account for the recent behaviour of Arctic sea ice. Not only are the CCSM4 models systematically high, but they fail in more important ways. If the recent declines are due to natural variability, the CCSM4 models appear unable to reproduce such extreme variability (correct me if I’m wrong). Time shifting CCSM4 models does not fix this problem as it does not affect that variability. As for this figure for CCSM3 (http://psc.apl.washington.edu/wordpress/wp-content/uploads/schweiger/ice_volume/validation/Fig13b.png), it omits 2010 & 2011, which means the PIOMASS is now well below the lower ensemble bound and declining much faster.

Alternatively if the recent collapse of sea ice is a genuine trend, the CCSM4 models are found even more wanting. Is it time to bin the CCSM3/4 models. I suspect yes.

There is nothing inherently superior in a complex non-linear model of a physical (or financial or biological or whatever) system that is, well wrong, either because it omits key processes or lacks fidelity to known processes. @350limit’s comments (#54) on the failure of econometric models capture this problem precisely.

It is very easy to be deceived by the inherent beauty and self-consistency of your models, plus all the time invested in them. But without evidence of their fidelity to reality how can they be given more credence than short-term extrapolations?

Thanks Tenney, I had seen that, but the closest it seems to get is saying “a substantial amount of CH4 released at the seaﬂoor in the shallow ESAS is delivered to the atmosphere (Shakhova et al 2010b, 2010c).” — is that documented in the 2010 papers?

During the melt season the albedo of seasonal ice is less than multiyear
Seasonal ice absorbs and transmits more sunlight to ocean than multiyear
Albedo evolution of seasonal sea ice has 7 phases

Authors:

Donald K. Perovich

Christopher Mark Polashenski

There is an ongoing shift in the Arctic sea ice cover from multiyear ice to seasonal ice. Here we examine the impact of this shift on sea ice albedo. Our analysis of observations from four years of field experiments indicates that seasonal ice undergoes an albedo evolution with seven phases; cold snow, melting snow, pond formation, pond drainage, pond evolution, open water, and freezeup. Once surface ice melt begins, seasonal ice albedos are consistently less than albedos for multiyear ice resulting in more solar heat absorbed in the ice and transmitted to the ocean. The shift from a multiyear to seasonal ice cover has significant implications for the heat and mass budget of the ice and for primary productivity in the upper ocean. There will be enhanced melting of the ice cover and an increase in the amount of sunlight available in the upper ocean.

Perhaps this mechanism contributes to acceleration in ice volume loss between 1979 and the present. If it has not been included in models, then the departure seen in fig. 3 might be partly explained.

“@350limit’s comments (#54) on the failure of econometric models capture this problem precisely.”

Indeed. I find the responses to those comments very problematic. Hogwart’s? 350limit did not say to resort to magic rather than science, only that the authors have been blinded by the latter … I would refine that to refer to certain elements of scientific culture that tend toward extreme conservatism in making pronouncements or urging action — this isn’t science, it’s sociology, and the comments about “influencing policy” and “a particular political outcome” are also not science, or scientific.

Just what “particular political outcome” are we talking about? The scientific question is when will the Arctic be free of ice (not whether, although it’s possible that it will not be — if human activities change in a way not reflected in any of the models). The political debate is, crudely, whether to take action to mitigate global warming or not. No matter when the Arctic will be free of ice, the fact that it will be is just one of many scientific indications that mitigating action is desirable. How might possible answers to the scientific question about the Arctic ice influence the two policy approaches, the undesirable one and the desirable one? Well, the later the end of Arctic ice comes, the more people can rationalize not acting. It is therefore reasonable and rational to focus on the possibility (an accurate probability estimate is not required) of the earliest plausible estimate … especially when the models have repeatedly underestimated ice loss. This is the rational conclusion that one can see when one is not blinded by certain science-culture attitudes and takes into account factors outside of the science of Arctic sea ice … factors such as political inertia, principles of risk mitigation, and the enormity of that risk.

Following from #57:
John Nissen: Note that, even if the situation turns out not be quite so dire, action should be taken on the precautionary principle; see Douglas’s comment #33. Why do advising bodies, such as IPCC, always seem to rely on optimistic forecasts, based on out-of-date models, in order to inform politicians of the situation?

[Response: Would you have them report on anything other than the current state of the science? Everything in IPCC has to be traceable to peer-reviewed science, and so that is what they will reflect. Note too that IPCC does not advocate for policy which is what you appear to want it to do. – gavin]

In my opinion the IPCC is a solid scientific body. I respect that. Although it seems a rather slow grinding process, it’s a remarkable achievement to achieve the degree of consensus that they do.

However, where I think they are either wildly optimistic or (more fairly) where they clearly state their own limitations – is where they tend to include caveats in their reports. Caveats that seem to be overlooked by policy makers. For example, their estimates are clearly stated and explained – but if there is then a list of factors that they are unable to assess or accurately predict which may make the situation much worse, these are clearly stated. Just because we don’t understand the system fully and have to ignore portions of it to achieve consensus science doesn’t automatically mean those portions aren’t important and significant.

My issue is that people in general, and especially policymakers tend to focus on the most optimistic outlook of what the IPCC present, which is to ignore factors unable to be included, many of which have quite negative impacts.

As events unfold and predictions are proved by events to have been too far into the future, it suggests we are ignoring these caveats too much – at our peril. As an abstract scientific discussion – great – we’re learning the truth. Unfortunately, if events run much faster than expected it brings on an existential crisis.

I personally wouldn’t get on an aeroplane if there was even a 5% chance it would crash en route. In fact, I would regard an aeroplane with a 1% chance of crashing on route as very questionable. By my logic I’d apply the precautionary principle rather strongly if there was even a small chance of major effects such as near future sea ice loss…

The response to my previous post was reasonable however – can one present an explicable hypothesis with some quantification of the risks one is running – definitely food for thought there.

In one of his earlier responses to a comment, author Axel Schweiger extols a discussion by Neven Acropolis on his Arctic Sea Ice Blog about use of various sea ice graphs. It concerned a graph used by the Arctic Methane Emergency Group, attributed to PIOMAS. In fact, it was just based on PIOMAS data, not from them.

as it “shows all the different outcomes of different statistical approaches.”

I too think it is great, and heartily recommend to the Arctic Methane Emergency Group that henceforth they start using it in their literature.

The reason I mention the Arctic Methane Emergency Group (AMEG) in particular, is that Schweiger is hardly overly subtle that this whole blog piece is essentially written such as to be directed towards that group. The “Perils of Extrapolation” of the title finally comes to rest on a discussion of “extrapolation” which explicitly names AMEG at its outset, and concludes that, “Predictions of earlier ice-free dates (he means, before 2037) so far seem to be confined to conference presentations, media-coverage, the blogosphere, and testimony before to the UK parliament.” Of those four there are three links, one already used before from the sea ice blog – the other two are both from AMEG. At the beginning of his piece there was mention of “water cooler speculation,” and by this point in the article we know just who he was trying to demean.

But in fact, it is just in making this somewhat dismissive and pompous claim that Schweiger runs his argument completely aground. In 2007, when Al Gore spoke at his Nobel award ceremony, he mentioned the work of ice modeller Wieslaw Maslowski. Maslowski came under intense scrutiny afterwards, and in some interviews seemed at pains to discuss his modelling and its own prediction for a first ice-free September, which Gore had stated before the world (although the US press almost unanimously declined to include this in their articles about the speech) – could come by 2014. “Seven years,” Gore said in 2007, followed by a long silence. We’re still not quite there, so we don’t know. At times Maslowski seemed almost to retract or deny that this had been an accurate depiction of his own work. It has surely been a controversial kind of thing to say, let us all agree, and those who don’t like such predictions really don’t like them. Yet just last year, Maslowski and his group released an updated version of their sea ice model, which sees 2016 as the most likely year for such an arrival.http://www.sciencepoles.org/news/news_detail/maslowski_and_team_offer_new_estimate_summer_arctic_sea_ice_disappearance/

It has nothing to do with PIOMAS. But it is a model. It is hardly “water cooler speculation,” and I don’t know that I’d want to call it, “extrapolation.” In a way, even the use of that term becomes loaded in this article. I realize that Schweiger means this in a specific sense, but more generally, with all computer modeling, prediction of the future involves extrapolation. Period.

In one of his responses to a comment Schweiger says, ‘I think “might” has little useful scientific meaning.’ Unfortunately for him, in the real world, and specifically in policymaking, “might” must have meaning. One of the key problems in the last three decades of interaction between climate scientists and policymakers is that, under political pressure, those things that are highly uncertain – where the scientists’ ‘pdf curves’ look flat and flabby – tend to just get left out of the picture altogether. That makes for awful policy. It’s hard to take a sane or precautionary approach to problems if those rough and hazy “mights” of our future reality can’t be considered.

What Schweiger never quite deals with, in all his spiel about using his PIOMAS with hindcasts versus forecasts, etc, is that we simply don’t know what the sea ice volume is. He himself mentions that real data cannot tell us that because the data samples are far too slight. We know the area because we see it, but we can’t know the volume. But that means that we will never know what ice volume is for sure until it is zero. Of course that also means that AMEG doesn’t know when the ice will be gone, but they are really only saying that it might.

I think what is really bothersome here, almost offensive, is to see something like the agenda-driven tactics of Patrick Michaels and deniers right at the heart of establishment climate science. After all, it seems crystal clear what the underlying calculation was: since AMEG had engaged in “high profile extrapolation” using PIOMAS, and Real Climate found this not to be judicious, they decided to take them down and discredit them by getting those who have been working inside PIOMAS modelling to say that they had misused or misunderstood the model. And yet, while PIOMAS data was being used by AMEG, they actually didn’t need to use it for their fundamental argument at all – they could have used Maslowski’s model, for example. In this way, Schweiger never actually addresses the fundamental arguments involved in AMEG, since he’s only a modeller caught up in how to interpret his model. Undoubtedly, he’s highly competent and professional at that.

At the beginning Schweiger takes his hat off to recently deceased Norbert Untersteiner, without whom, he acknowledges, his PIOMAS model couldn’t have been created. But as Maslowski quotes from Untersteiner, “A linear increase in heat in the Arctic Ocean will result in a nonlinear, and accelerating, loss of sea ice.” And such non-linear accelerations are of course notoriously tough to pin down: how much will the acceleration accelerate?

Schweiger concludes, “The answer will have to come from fully coupled climate models. Only they can account for the non-linear behavior of the trajectory of the sea ice evolution and put longer term changes in the context….”

Here’s the reason I agree with Neven that the graph with PIOMAS combined with different statistical curves is the best one, and AMEG should use it. In a Malcolm Gladwell “blink” kind of way, you can just cast your eye at this picture of different approaches and grasp the whole situation. I actually agree with one of Schweiger’s points, that natural variability must be kept in mind, if one is trying to make a very specific year of arrival prediction. But one can see in this graph in an instant that while the ice MIGHT do different things, currently its trajectory is towards rapid collapse, and one senses immediately that Schweiger might still be in the middle of his unendingly bland sentence – “this analysis will change the predicted timing of the “ice free summer” but large uncertainties will likely remain…..blah, blah” whenever we might happen to get there.

I’d like to react to Gavin’s first response to my posting #57, since it is relevant to the modelling. I had said that the main forcing for sea ice retreat was from the albedo flip.

He wrote the following:

“Response: This actually doesn’t make much sense. The energy for ice melting is coming from mainly long wave effects and some ocean heat transports, the albedo effect adds to that of course (but less than you might think because of cloud compensation), but a doubling of the albedo effect will not double the rate of melting. And since this is all standard stuff, it is all included in the GCMs, and yet they don’t show the kind of behaviour you are postulating. – gavin]”

It seems to me that modelling has to take account of the physical processes and the data. Gavin disputes that the main driver of the sea ice retreat is the albedo flip, but we are seeing not only polar amplification of global warming but positive feedback, which would not be explained simply by radiative forces and ocean currents. Positive feedback produces a non-linear trend, and the exponential and logarithmic trends fit the PIOMAS data well. This supports my contention that the albedo flip effect could be dominant.

Those who think that there’s nothing to worry about, because sea ice might recover on its own accord, are requiring some negative forcing or feedback effect to come into play, to make the PIOMAS trend line do a U-turn. There is no sign of such an effect. Somebody has described this as waiting for a Unicorn!

David Archer’s argument that there’s nothing to worry about methane is based on the continued existence of the sea ice. If you look at the evidence (as opposed to models) of methane from the Arctic seabed, as collected by Shakhova and Semiletov, then you have to treat the possibility of methane escalation extremely seriously.

So, if we don’t want the sea ice to disappear, we have to do something – we have to intervene. And this is where the geoengineering comes in. Some people argue that to geoengineer would be premature, but do we sit back and do nothing? Unfortunately rescue by emissions reduction is far too late (see Titanic comment 22#). We are forced to find methods of rapidly cooling the Arctic to deal with what appears, from the best evidence, to be a planetary emergency.

Take the ideal situation, in which we have a series of parallel Earths. It’s only in our Earth that Pinatubo erupted in 1991, there was a mega-El-Nino in 1998, and ocean currents initiated and weather maintained conditions for the sea-ice Crash of 2007. In other Earths these events may have happened at different times or not at all (so far). We don’t have that ideal situation, so we have to rely on models as the next best option. However the principle applies, in free-running* models the events I listed won’t happen or will happen at different times. * I say free-running to differnentiate them from assimilating models.

You mention CCSM4 being even further out if 2010 and 2011 PIOMAS volume is included. In that claim you are clearly unware that the volume losses of both those years are due to specific time-limited incidents, and it is hard to eliminate weather as a cause of those events (as Dr Schweiger points out). So the very years you concentrate on to bolster your claims could actually fit into the paradigm of “Reality is only one realization of an ensemble”.

It is very easy to be deceived by the inherent beauty and self-consistency of your models, plus all the time invested in them. But without evidence of their fidelity to reality how can they be given more credence than short-term extrapolations?

Then why are you paying any attention at all to PIOMAS? Since 2007 it has shown a continued volume loss, over that period the trend in observed thickness, while patchy and sporadic, is rather equivocal. This is no surprise as the thickness reductions implied by PIOMAS are small up to 2009. Again it is only 2010 and 2011 that imply substantial thickness reductions, and so far I’ve not found public widespread data on Arctic thickness that covers these years. So unless you can enlighten me, I see no empirical evidence that strongly supports what PIOMAS is telling us for the post 2007 era.

That said, having read most of the literature on PIOMAS I am confident that the losses of 2010 and 2011 are, on balance of probabilities, real. However I am also confident that free-running models as in CCSM4 are capable of informing debate, even though I personally expect the Arctic to have a September minimum of less than 1M km^2 some time next decade. From my reading of the evidence it’s only if PIOMAS shows another massive Spring volume loss that I’ll be entertaining any suspicion at all that we may see such a state this decade. This is because the ‘early camp’ are missing a major factor, even though most of them don’t know it: That factor is that first year sea ice will continue to grow to thicknesses of around 1.5 to 2m through the winter, so the key issue in whether September can be virtually sea-ice free is how much sea ice can be lost between March and September. The warming being seen during the Autumn and Winter is mainly due to increased heat fluxes from the surface (Screen & Simmonds 2010) due to thinner ice and more open water, so represents a net heat loss to the atmosphere. Whilst the arguments of Abbot, Pierrehumbert and others about cloud radiative forcing may imply an earlier winter low ice condition than GCMs suggest, there is no evidence that this will kick in within a few years. So you’re stuck with a situation in which Winter ice growth remains vigorous and heat losses to the atmosphere increase as the ice recedes.

If the Spring losses are not due to weather but are due to some other factor then they hold open the possibility that increasing and maintained Spring losses could be enough to increase the overall melt season loss so as to leave the Arctic virtually sea ice free by September. Lacking an understanding of what is hapening if we see these losses in three consecutive Springs it is reaonable to begin to suspect that this is not a random process such as weather, but something new. This new factor could be weather but also be part of a new process (the Arctic Dipole being a classic case), or it may be something connected with ice or ocean processes. But three years of a maintenance of a behaviour probably intiated by weather would make the situation seem more analgous to 2007.

The point is, nobody know yet what this Spring and future Springs will bring. Even if this does turn out to be a new factor, that doesn’t bolster the use of extrapolation of trends because a new factor isn’t accounted for by preceding years.

#62–Reading Tietsche again, I see what you mean, Kevin. However, I think my main point stands (and its essence was better expressed in the inline to your original comment at #31): the perturbations in Tietische aren’t comparable to the decline of 2007. The former magically ‘erased’ all sea ice on July 1; the latter saw it decrease dramatically due to physical ‘forcings.’ Thus the 2007 was not greatly different from ‘what the climate wanted it to be,’ to use the anthropomorphic but useful image from the inline.

I’m certainly repeating others in this thread (& I’ve said it elsewhere before) when I say that the difference between CCSM4 AR4 & PIOMAS outputs is surely far too large to be dismissed as “natural variability” or for such variability to be even a major reason for such a large difference. I should add that I do see the value of models but cannot accept without comment the post’s conclusion “…we need to let science run its course and let previous model-based predictions of somewhere between “2040 and 2100″ stand.”
(I could be sarcy and say that at the present rate of advancement, the science will be hard pushed to reach its conclusions on the date of an ice-free summer Arctic Ocean prior to the event occuring!)

One place in the post that I felt deficient was Figure 4 which lacked a “We are here” marker. (It would be at the top & slightly left of the ‘1’ in “…Member Sigmoid Fit 1979-…”) That ‘deficiency’ in Figure 4 reminded me of a graph linked at Neven’s site which has such a “We are here” marker superimposed onto a now-outdated RealClimate graph, only that graph was for Extent not Volume & so importantly compares a measured quantity rather than a model output as is PIOMAS.Extent graph linked here.

Considering ice volumes over the last decade, CCSM4 AR4 is predicting a decline of some 200 cu km pa while PIOMAS models show some 600 cu km pa. That’a a big difference & surely has to be the result of something noticable. (It’s an extra 0.13 zJ pa required to do the extra melt, a figure big enough to get the hardest hearted skeptic to sit up & take notice. Yes! Gather that lot up and bottle it & you could power the entirity of the good old US of A with it! And some.)

That the CCSM4 AR4 are out for Extent as well as Volume got me reaching for the ‘back of my fag packet’ & the naive result may hold arithmetical error but certainly caught my attention.
Half a million less ice extent is a lot more extra open ocean with far lower albedo. My simple model suggests 0.28 zJ pa energy from that reduction.

I do understand the model-speak of the post. Predicting the ice-free summer Arctic Ocean isn’t what drives their effort. Yet work improving the modelling must surley also, with little extra work, a better answer to the ‘ice-free’ question than “natural variability.”

I’m certainly repeating others in this thread (& I’ve said it elsewhere before) when I say that the difference between CCSM4 AR4 & PIOMAS outputs is surely far too large to be dismissed as “natural variability.” I should add that I do see the value of models but cannot accept without comment the post’s conclusion “…we need to let science run its course and let previous model-based predictions of somewhere between “2040 and 2100″ stand.”
(I could be sarcy and say that at the present rate of advancement, the science will be hard pushed to reach its conclusions on the date of an ice-free summer Arctic Ocean prior to the event occurring!)

One place in the post that I felt deficient was Figure 4 which lacked a “We are here” marker. (It would be at the top & slightly left of the ‘1’ in “Ensemble Member Sigmoid Fit 1979-2011.”) That ‘deficiency’ in Figure 4 reminded me of a graph linked at Neven’s site which has such a “We are here” marker superimposed on a now-outdated RealClimate graph, only that graph was for Extent not Volume & so importantly compares a measured quantity rather than a model output as is PIOMAS.Extent graph with ‘marker’ here.

Considering ice volumes over the last decade, CCSM4 AR4 is predicting a decline of some 200 cu km pa while PIOMAS models suggest something more like 600 cu km pa. The energy to do that extra melt would be 0.13 zJ pa. That’s a lot of energy & surely has to be the result of something noticeable. (Heck, even the hardest hearted skeptic would sit up hearing a figure like that. Gather it up and bottle that energy and you could power the whole of the good old US of A and some!)
The thought that CCSM4 AR4 is also overestimating Extent as well as Volume got me reaching for ‘the back of my fag packet’ & while the number produced may be wrong somewhere, it certainly gave me food for thought. For half a million sq km less Extent, my naive model gave 0.28 zJ pa.

I do understand answering the ‘ice-free’ question is not the purpose of the models. Yet work improving the models must surely allow a quick & easyish answer that is an improvement to the “natural variability” answer. And I strongly believe the question merits such an answer.

Marcel,
The threats we face from climate change have been amply demonstrated to any sane person willing to take a look at the science. As such, I am puzzled why you think that an additional alarming prediction would suddenly lead humanity to a collective “Come to Jebus” moment and to action on the problem. This is particularly so when the science regarding said prediction remains on the cutting edge and therefore uncertain.

I believe we are facing a force that surpasses inertia–namely collective human stupidity. People will simply refuse to confront a threat if they don’t see a clear path to surmounting it–and preferably one that allows them to keep all their comforts, luxuries and follies. The only force that I know of that can counter that stupidity is human creativity. The few actually intelligent humans will have to bail the rest of humanity out–as they have done before several times.

It seems that human survival now depends on a struggle between the two tails of the intelligence bell curve.

@62 Ice recovers to the “reference run” which does have the greenhouse gas forced decline. Not sure what the confusion is.
@68 Marcel. Did we make a policy statement? I don’t think so. I think we have plenty of good evidence for taking action as it is, but that’s my personal opinion. Whether or not it is helpful to push scientifically unsupported scenarios is indeed beyond my expertise.
@72
Kevin,
To continue the anthropomorphism: My view of the consensus is that the 2007 extent anomaly was where “both the weather and the climate wanted it to be”. Note also that the 2007 thickness anomaly wasn’t as dramatic as the extent anomaly. See: http://psc.apl.washington.edu/lindsay/pdf_files/Lindsay%20etal%202009%20JClim%20-%202007%20follows%20thinning%20trend.pdf

Has anyone asked his group directly if they are relying on information not available to other scientists? Or are they providing somewhat laundered data? It’s been a long, long time since the first “Gore Box” data release.

I wonder if the modeling by the Navy Postgraduate School may be relying on some undisclosed thickness data. (From a military perspective, you’d have to hope so! but for science, not.)

The language is subtle; is the data all available if you know who to ask?

“The skill of the model is examined by comparing its output to sea ice thickness data gathered during the last two decades. The first dataset used is the collection of draft measurements conducted by U.S. Navy submarines between 1986 and 1999. The second is electromagnetic (EM) induction ice thickness measurements gathered using a helicopter by the Alfred Wegener Institute in April 2003. Last, model output is compared with data collected by NASA’s ICESat program using a laser altimeter mounted on a satellite of the same name.
The NPS model indicates an accelerated thinning trend in Arctic sea ice during the last decade. The validation of model output with submarine, EM and ICESat data supports this result. This lends credence to the postulation that the Arctic not only might, but is likely to be ice-free during the summer in the near future.”

“The model comparison is made against the most recently released collection of Arctic ice draft measurements conducted by U.S. Navy submarines between 1979 and 2000.
The NPS model indicates an accelerated thinning trend in Arctic sea ice during the last decade. The validation of model output with submarine upward-looking sonar data supports this result. This lends credence to the postulation that the Arctic is likely to be ice-free during the summer in the near future.”

How about the British and Russian and French Navy submarine fleets? — have they been asked to provide researchers with any Arctic ice thickness data?

How about the oil and gas companies that are starting to explore the Arctic? Is there a point where commercially proprietary data should be released in the public interest?

Well, it’s pretty hard to argue against ‘the ice is where the climate wants it to be…” It is where it is. :)

Nor am I trying to equate 2007 with removing all the ice per Tietsche’s model runs. What I am trying to decipher are the physical mechanisms that make Tietsche’s results possible.

It seems logical that any large perturbation in ice extent is going to lead to certain mechanisms – Chris R details a couple of them in #75. Indeed after 2007 we *did* see an upwards blip in extent. If sea-ice extent were our only measure or concern I would say that Tietsche was a very good explanation.

BUT … Tietsche also says that volume recovers just as quickly as extent. This is where (for me) the story falls apart. If volume had blipped upwards following 2007 and then resumed its downward trend there would be no argument (from me) on the validity of the model.

Instead we see volume ignore the large perturbation. This indicates *something* – a flaw in the model, a flaw in our volume data collection (PIOMAS), a ‘perfect storm’ of weather following 2007 (??), or a basic misunderstanding of the sensitivity of sea-ice volume to climate.

My own amateur suspicions are that Rampal and Kay have already highlighted two of the major model failings in regards to sea-ice modelling: ice mechanics and kinematics and cloud forcing/feedbacks.

I’m not arguing that Tietsche is wrong vis a vis ‘tipping points’ – I’m arguing that climate *is* driving sea ice loss and that the model Tietsche used lacked the proper mechanisms to reliably extrapolate forwards. What would the model runs look like if the deficiencies Rampal and Kay noted were addressed? Until that time I can only regard Tietsche as interesting, not definitive or authoritative.

“The first of its kind ICEX survey has proved to be of great value to both NASA and NRL in terms of better understanding the capabilities of airborne and satellite based instruments to measure varying ice types. This will aid in achieving a resolution that is adequate to minimize the degree of uncertainty in models that forecast future conditions and for monitoring decadal variability.”

“Abstract : The Chinese are increasingly interested in the effects of global climate change and the melting of the Arctic ice cap, especially as they pertain to emergent sea routes, natural resources, and geopolitical advantage. China seems to see the overall effect of Arctic climate change as more of a beckoning economic opportunity than a looming environmental crisis. Even though it is not an Arctic country, China wants to be among the first states to exploit the region’s natural resource wealth and to ply ships through its sea routes, especially the Northwest Passage….
…
… This study considers at some length the discussions and debates on Arctic issues, mainly in Chinese-language scholarly journals but also in journalistic and diplomatic Chinese-language discussion. The study is a report on China’s sometimes-contentious debates and discussions of the issue, an account that hopes to convey something of their extent, complexity, and flavor while China works out its Arctic policy and prepares for its future position in and regarding the Arctic. It also offers some foreign policy recommendations for the United States.”

It is also unlikely that first year ice will continue to grow to thicknesses of 1.5-2.0 m through the winter when the temperature of the Arctic Ocean is steadily increasing, wind speeds are increasing, the first year ice is increasingly briny and brittle and easily broken up, and when there appears to be an increasing tendency toward longer and steeper negative AOs, meaning that warmer air and water will be entering the Arctic Sea via the North Atlantic for extended periods of time.

If I understand AMEGs argument correctly, it is that we need to find engineering solutions in the Arctic to alleviate an effective emergency (on a basis of precautionary principle at very least) posed by possible majority loss of sea ice or escalation in methane release. I’d be rather surprised if they weren’t also recommending to resolve atmospheric carbon dioxide burden as well? (quite a few people are already correctly calling for that)

I happen to generally agree with them – in my view the situation is somewhat like a car heading for a cliff with faulty brakes. While we do need to fix the brakes (carbon dioxide), the imperative ought to be on stopping the vehicle (by any means we can) before we drive off the cliff (catastrophic climate change). The brakes should have been fixed a long time ago now. Fixing them now won’t necessarily leave us time to avoid the cliff (inertia in the system).

For the submarine data, I think that whatever USA has collected is more or less what there is.

USAs submarines, in order to hold targets deep inside Soviet/Russia at risk, did routinely hold station under the arctic ice for extended periods, and the ice above had to be something they could penetrate, so they measured it.

English and French submarines have not, as far as I know, held target at risk from below ice on a routine basis, so whatever data they have is probably very sparse, they also have very few submarines in total.

Soviet/Russia has to my knowledge never operationally held targets at risk from below ice, there being no need to do so, as their targets were reachable from positions in the Atlantic and Pacific.

I have no idea if Soviets atomic icebreaker(s) collected data which could be useful, one of them is still in action I belive, so call them and ask…

I agree with your assessment of Tietsche et al in #62. The finding with regards volume being apparently at odds with the volume loss suggested by PIOMAS seems relevant to Tim Lenton’s recent pronouncements.http://www.newscientist.com/article/mg21328583.900-arctic-sea-ice-may-have-passed-crucial-tipping-point.html
A point Lenton has been making is that after 2007 instead of a recovery to a pseudo equilibrium state there has been a continuation of the greater seasonal cycle. If we are to accept the PIOMAS volume loss, this adds to Lenton’s point.

As I’ve blogged recently I still think Boe et al presents the most comprehensive model mechanism that could explain the failure of models to reproduce the observed change. The Kay et al paper you link to specifically refers to the occurrence of stable summer conditions in one model. While Rampal et al may not explain the difference so much because of the observed difference between recent volume and area transport through Fram. There is no trend in net mass of ice transported through the Fram Strait.http://www.ifm.zmaw.de/research/remote-sensing-assimilation/sea-ice/sea-ice-volume-flux/
Although due to increase of speed of transport through Fram, area flux has increased in recent years (Smedsrud et al, 2008). So whilst the ice is more mobile because it’s thinner, the fact that it’s thinner means less volume is transported, the two factors balancing to give no increase in volume transport.

Seriously, from what we know, so many other nasty things will have happened before a methane burp becomes an issue.

The “methane emergency” is like a movie car-over-cliff-explodes scene.
In reality cars don’t explode; the crash kills the passengers anyhow..

But imagine there’s a decision to cool the Arctic — well, what tactics might cool the Arctic?

— forbid aircraft in the stratosphere, keeping it dry, removing contrails
— forbid diesel shipping so black carbon isn’t deposited on the ice
— supplement nutrients that limit methanogens and photosynthesizers (stir up sediments? Or are we flushing excess nutrients from the rivers around the Arctic?)
— increase the emissivity of the Arctic ocean or sea ice below a clear dry sky?
— warm the upper atmosphere so it radiates more heat away? As the stratosphere has been cooling, we get Arctic ozone holes ….
— halt fossil fuel use and invest in alternatives as fast as possible

From what I understand, the geoengineering being considered is to spray water or salt water into the atmosphere to increase the reflectivity of clouds. The method is being worked out by Dr. Stephen Salter.

What controls primary production in the Arctic Ocean? Results from an intercomparison of five general circulation models with biogeochemistry

Key Points
Models show similar features in terms of the distribution of primary production
However, physical factors controlling this distribution differ between the models
Models disagree about which factors, light or nutrients, control present productivity

Wait, dagnabbit, this is one the submariners certainly can answer, if they don’t already have the information. Do the various Navy submarines sample the water they’re going through regularly? (And if not wtf not, eh?) So they should be able to answer the question that paper stopped with, and this would be a major clue toward _bio_engineering the Arctic:

“The intercomparison between models finds substantial variation in the depth of winter mixing, one of the main mechanisms supplying inorganic nutrients over the majority of the AO. Although all models manifest similar level of light limitation owing to general agreement on the ice distribution, the amount of nutrients available for plankton utilization is different between models. Thus the participating models disagree on a fundamental question: which factor, light or nutrients, controls present-day Arctic productivity. These differences between models may not be detrimental in determining present-day AO primary production since both light and nutrient limitation are tightly coupled to the presence of sea ice. Essentially, as long as at least one of the two limiting factors is reproduced correctly, simulated total primary production will be close to that observed. However, if the retreat of Arctic sea ice continues into the future as expected, a decoupling between sea ice and nutrient limitation will occur, and the predictive capabilities of the models may potentially diminish unless more effort is spent on verifying the mechanisms of nutrient supply. Our study once again emphasizes the importance of a realistic representation of ocean physics, in particular vertical mixing, as a necessary foundation for ecosystem modeling and predictions.”

Hell, the petroleum companies probably also have this kind of water contents data collected, for any area they’re interested in, whether they’ve looked at whatever samples they collected or not.

Isn’t this a place where rather than just improving the models, getting hold of actual physical and biological data about the ocean over time will help?

The Arctic has been used for decades by submarines doing their cold war best to know everything they might possibly need to survive and outlive their opponents. This kind of data about currents and mixing and water chemistry and even plankton samples just be in the various Navies’ systems somewhere, if only as raw samples on dusty shelves.

‘am puzzled why you think that an additional alarming prediction would suddenly lead humanity to a collective “Come to Jebus” moment’

Your puzzlement is your own doing, since I didn’t say that.

‘and to action on the problem’

What I said was that “the later the end of Arctic ice comes, the more people can rationalize not acting”. If you manage to grasp the difference between what I wrote and what you have, perhaps you will become less puzzled. Notably the people whose rationalization in this area most matters are people in positions of political power.

‘The few actually intelligent humans will have to bail the rest of humanity out–as they have done before several times.

It seems that human survival now depends on a struggle between the two tails of the intelligence bell curve.’

I expect someone on the high end of the curve to notice the contradiction between those statements. And I don’t think either of them are accurate.

@Axel Schweiger

“Did we make a policy statement?”

You made statements about policy; I quoted from them. Regardless of any quibble about whether they are policy statements, they are not scientific statements about climate or ice.

“push scientifically unsupported scenarios”

Like Ray you project on me a position I did not express. I referred to plausible scenarios … plausible on the basis of the available evidence. Such plausible scenarios are supported by science, even if they haven’t been proven by science … but science isn’t in the business of proof or final absolute authority … and if that’s what it were to take to be scientifically supported, then science would be useless for policy … but fortunately is not. Considering only the science, there is some earliest plausible date of an ice free Arctic summer, and that date should be of considerable interest to policy makers, and that date, being a boundary, has a special status, purely within a logical and scientific framework.